{"id":"W2290671243","doi":"10.1016/j.cognition.2016.02.013","title":"Unusual hand postures but not familiar tools show motor equivalence with precision grasping","year":2016,"lang":"en","type":"article","venue":"Cognition","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Body schema; Schema (genetic algorithms); Psychology; Thumb; Motor control; Proprioception; GRASP; Human–computer interaction; Artificial intelligence; Computer science; Kinesthetic learning; Motor program; Communication; Computer vision; Machine learning; Neuroscience; Perception; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001537215,0.0001357894,0.0001180921,0.0001028804,0.00018866,0.00007535136,0.00009175731,0.0001277748,0.0009904607],"category_scores_gemma":[0.0002146836,0.00009299712,0.0000358631,0.0001568888,0.00009770486,0.0004880044,0.00002088704,0.00007984493,0.0005430261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005609042,"about_ca_system_score_gemma":0.00003072233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007723001,"about_ca_topic_score_gemma":0.00001324839,"domain_scores_codex":[0.9989028,0.0001017038,0.0002155897,0.0003268375,0.0002541126,0.0001989446],"domain_scores_gemma":[0.9991173,0.0001948988,0.0001319193,0.0001970416,0.0002923199,0.00006650264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001429575,0.0002398333,0.005427314,0.00003449754,0.0000870763,0.00002900295,0.0009028218,0.000008332125,0.4749564,0.0024813,0.003896895,0.510507],"study_design_scores_gemma":[0.005932198,0.001440716,0.8198233,0.0006734963,0.0001297907,0.0001198532,0.001176317,0.0003440378,0.1580876,0.001682337,0.009804646,0.0007856606],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7456014,0.00003373169,0.245609,0.0006878736,0.0005683745,0.0003810625,0.00005360398,0.0001330938,0.006931955],"genre_scores_gemma":[0.9940538,0.00003474494,0.0002460019,0.0007255921,0.0002279036,0.00006617948,0.00008333509,0.00002262238,0.004539834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.814396,"threshold_uncertainty_score":0.9999228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07030984465876458,"score_gpt":0.3147914423230493,"score_spread":0.2444815976642847,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}